Homo-junction bottom-gate amorphous In-Ga-Zn-O TFTs with metal induced source /drain regions

A fabrication process for homo-junction bottom-gate (HJBG) amorphous In–Ga–Zn–O (a-IGZO) thin-film transistors (TFTs) is proposed, in which the a-IGZO section as source/drain (S/D) region is induced to a low resistance state by coating a thin metal Al film and then performing a thermal annealing in oxygen, and that as channel region is protected from back etching by depositing and patterning a protective layer. Experimental results show that with a 5 nm Al film and a 200 ºC annealing, the sheet resistance of the S/D a-IGZO is 803 Ω/□ and keeps stable during subsequent thermal treatment. In addition, the annealing generated thin Al2O3 film contributes to improve the thermal stability and ambient atmosphere immunity of the fabricated HJBG TFTs. Please click Additional Files below to see the full abstract

Boundary integrated neural networks (BINNs) for 2D elastostatic and piezoelectric problems: Theory and MATLAB code

In this paper, we make the first attempt to apply the boundary integrated neural networks (BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric problems. BINNs combine artificial neural networks with the well-established boundary integral equations (BIEs) to effectively solve partial differential equations (PDEs). The BIEs are utilized to map all the unknowns onto the boundary, after which these unknowns are approximated using artificial neural networks and resolved via a training process. In contrast to traditional neural network-based methods, the current BINNs offer several distinct advantages. First, by embedding BIEs into the learning procedure, BINNs only need to discretize the boundary of the solution domain, which can lead to a faster and more stable learning process (only the boundary conditions need to be fitted during the training). Second, the differential operator with respect to the PDEs is substituted by an integral operator, which effectively eliminates the need for additional differentiation of the neural networks (high-order derivatives of neural networks may lead to instability in learning). Third, the loss function of the BINNs only contains the residuals of the BIEs, as all the boundary conditions have been inherently incorporated within the formulation. Therefore, there is no necessity for employing any weighing functions, which are commonly used in traditional methods to balance the gradients among different objective functions. Moreover, BINNs possess the ability to tackle PDEs in unbounded domains since the integral representation remains valid for both bounded and unbounded domains. Extensive numerical experiments show that BINNs are much easier to train and usually give more accurate learning solutions as compared to traditional neural network-based methods

Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences.

The N6-methyladenosine (m6A) modification is deposited to nascent transcripts on chromatin, but its site-specificity mechanism is mostly unknown. Here we model the m6A deposition to pre-mRNA by iM6A (intelligent m6A), a deep learning method, demonstrating that the site-specific m6A methylation is primarily determined by the flanking nucleotide sequences. iM6A accurately models the m6A deposition (AUROC = 0.99) and uncovers surprisingly that the cis-elements regulating the m6A deposition preferentially reside within the 50 nt downstream of the m6A sites. The m6A enhancers mostly include part of the RRACH motif and the m6A silencers generally contain CG/GT/CT motifs. Our finding is supported by both independent experimental validations and evolutionary conservation. Moreover, our work provides evidences that mutations resulting in synonymous codons can affect the m6A deposition and the TGA stop codon favors m6A deposition nearby. Our iM6A deep learning modeling enables fast paced biological discovery which would be cost-prohibitive and unpractical with traditional experimental approaches, and uncovers a key cis-regulatory mechanism for m6A site-specific deposition

The Arabidopsis NLP7 gene regulates nitrate signaling via NRT1.1-dependent pathway in the presence of ammonium.

Nitrate is not only an important nutrient but also a signaling molecule for plants. A few of key molecular components involved in primary nitrate responses have been identified mainly by forward and reverse genetics as well as systems biology, however, many underlining mechanisms of nitrate regulation remain unclear. In this study, we show that the expression of NRT1.1, which encodes a nitrate sensor and transporter (also known as CHL1 and NPF6.3), is modulated by NIN-like protein 7 (NLP7). Genetic and molecular analyses indicate that NLP7 works upstream of NRT1.1 in nitrate regulation when NH4+ is present, while in absence of NH4+, it functions in nitrate signaling independently of NRT1.1. Ectopic expression of NRT1.1 in nlp7 resulted in partial or complete restoration of nitrate signaling (expression from nitrate-regulated promoter NRP), nitrate content and nitrate reductase activity in the transgenic lines. Transcriptome analysis revealed that four nitrogen-related clusters including amino acid synthesis-related genes and members of NRT1/PTR family were modulated by both NLP7 and NRT1.1. In addition, ChIP and EMSA assays results indicated that NLP7 may bind to specific regions of the NRT1.1 promoter. Thus, NLP7 acts as an important factor in nitrate signaling via regulating NRT1.1 under NH4+ conditions

Environmental impact of the tourism industry in China: analyses based on multiple environmental factors using novel Quantile Autoregressive Distributed Lag model

This study examines the impact of tourism on China’s environmental quality under the framework of the Environment Kuznets Curve. In this study, tourism is measured by the number of tourist arrival and environmental pollution is measured by three proxies: carbon emissions, atmospheric particulate matter, and greenhouse gases. The study additionally controls trade openness effects using annual data from 1995 to 2018. Based on the asymmetric behavior of environmental variables, the study applies the Quantile Autoregressive Distributed Lag model that helps to integrate both dynamic trends and non-linearity. The findings confirmed the validity of Environment Kuznets in the long run and unveiled that tourist arrivals reduce carbon emissions, atmospheric particulate matter, and greenhouse gases in the long run, but in short-run dynamics, tourist arrivals only reduce carbon emissions. Similarly, trade openness increases carbon emissions, atmospheric particulate matter, and greenhouse gases at initial quantiles in the long run. In contrast, in the case of the short run, trade openness reduces atmospheric particulate matter and greenhouse gases. These results imply that the emissions mitigating (contributing) effects of tourism and trade varied across lower and higher quantiles. In conclusion, the findings reveal that the government should take effective measures to implement appropriate strategies required to sustain tourism and trade in China

The Role of Mancala Games in Human Evolution, Cultural Development, and Education: An Anthropological Inquiry

Mancala games, a diverse category of traditional board games played across cultures worldwide, bear resemblances to chess in their significance to human evolution and cultural progress. Drawing from anthropological investigations, this research uncovers the adaptability of mancala games to farming, marine, and nomadic cultures, tracing their origins to ancient Egypt and their subsequent spread among diverse civilizations in Africa, Malaysia, and China. This study highlights the extensive contributions of mancala games across various educational domains, encompassing moral, intellectual, physical, and aesthetic education, all of which foster comprehensive human development. By examining the cultural, historical, and educational dimensions of mancala games, this research unveils their profound impact on human societies throughout history, shedding light on their enduring significance. Mancala games emerge as powerful tools for promoting moral reasoning, critical thinking abilities, strategic planning skills, physical coordination, and cultural understanding. They embody cultural traditions, transmit ancestral knowledge, and foster social cohesion. Through their entertainment value and ability to shape and enrich human experiences, mancala games weave a cultural tapestry that intertwines with human societies, both past and present. &nbsp